{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=8, releaselevel='final', serial=0)\n",
      "matplotlib 3.6.3\n",
      "numpy 1.23.5\n",
      "pandas 1.5.2\n",
      "sklearn 1.2.0\n",
      "torch 1.13.1+cu117\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "    \n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _california_housing_dataset:\n",
      "\n",
      "California Housing dataset\n",
      "--------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 20640\n",
      "\n",
      "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
      "\n",
      "    :Attribute Information:\n",
      "        - MedInc        median income in block group\n",
      "        - HouseAge      median house age in block group\n",
      "        - AveRooms      average number of rooms per household\n",
      "        - AveBedrms     average number of bedrooms per household\n",
      "        - Population    block group population\n",
      "        - AveOccup      average number of household members\n",
      "        - Latitude      block group latitude\n",
      "        - Longitude     block group longitude\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "This dataset was obtained from the StatLib repository.\n",
      "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
      "\n",
      "The target variable is the median house value for California districts,\n",
      "expressed in hundreds of thousands of dollars ($100,000).\n",
      "\n",
      "This dataset was derived from the 1990 U.S. census, using one row per census\n",
      "block group. A block group is the smallest geographical unit for which the U.S.\n",
      "Census Bureau publishes sample data (a block group typically has a population\n",
      "of 600 to 3,000 people).\n",
      "\n",
      "An household is a group of people residing within a home. Since the average\n",
      "number of rooms and bedrooms in this dataset are provided per household, these\n",
      "columns may take surpinsingly large values for block groups with few households\n",
      "and many empty houses, such as vacation resorts.\n",
      "\n",
      "It can be downloaded/loaded using the\n",
      ":func:`sklearn.datasets.fetch_california_housing` function.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
      "      Statistics and Probability Letters, 33 (1997) 291-297\n",
      "\n",
      "(20640, 8)\n",
      "(20640,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "housing = fetch_california_housing()\n",
    "print(housing.DESCR)\n",
    "print(housing.data.shape)\n",
    "print(housing.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array([[ 8.32520000e+00,  4.10000000e+01,  6.98412698e+00,\n",
      "         1.02380952e+00,  3.22000000e+02,  2.55555556e+00,\n",
      "         3.78800000e+01, -1.22230000e+02],\n",
      "       [ 8.30140000e+00,  2.10000000e+01,  6.23813708e+00,\n",
      "         9.71880492e-01,  2.40100000e+03,  2.10984183e+00,\n",
      "         3.78600000e+01, -1.22220000e+02],\n",
      "       [ 7.25740000e+00,  5.20000000e+01,  8.28813559e+00,\n",
      "         1.07344633e+00,  4.96000000e+02,  2.80225989e+00,\n",
      "         3.78500000e+01, -1.22240000e+02],\n",
      "       [ 5.64310000e+00,  5.20000000e+01,  5.81735160e+00,\n",
      "         1.07305936e+00,  5.58000000e+02,  2.54794521e+00,\n",
      "         3.78500000e+01, -1.22250000e+02],\n",
      "       [ 3.84620000e+00,  5.20000000e+01,  6.28185328e+00,\n",
      "         1.08108108e+00,  5.65000000e+02,  2.18146718e+00,\n",
      "         3.78500000e+01, -1.22250000e+02]])\n",
      "--------------------------------------------------\n",
      "array([4.526, 3.585, 3.521, 3.413, 3.422])\n"
     ]
    }
   ],
   "source": [
    "# print(housing.data[0:5])\n",
    "import pprint  #打印的格式比较 好看\n",
    "\n",
    "pprint.pprint(housing.data[0:5])\n",
    "print('-'*50)\n",
    "pprint.pprint(housing.target[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11610, 8) (11610,)\n",
      "(3870, 8) (3870,)\n",
      "(5160, 8) (5160,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#拆分训练集和测试集，random_state是随机种子,同样的随机数种子，是为了得到同样的随机值\n",
    "x_train_all, x_test, y_train_all, y_test = train_test_split(\n",
    "    housing.data, housing.target, random_state = 7)\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "    x_train_all, y_train_all, random_state = 11)\n",
    "# 训练集\n",
    "print(x_train.shape, y_train.shape)\n",
    "# 验证集\n",
    "print(x_valid.shape, y_valid.shape)\n",
    "# 测试集\n",
    "print(x_test.shape, y_test.shape)\n",
    "\n",
    "dataset_maps = {\n",
    "    \"train\": [x_train, y_train],\n",
    "    \"valid\": [x_valid, y_valid],\n",
    "    \"test\": [x_test, y_test],\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "\n",
    "class HousingDataset(Dataset):\n",
    "    def __init__(self, mode='train'):\n",
    "        self.x, self.y = dataset_maps[mode]\n",
    "        self.x = torch.from_numpy(scaler.transform(self.x)).float()\n",
    "        self.y = torch.from_numpy(self.y).float().reshape(-1, 1)\n",
    "            \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        return self.x[idx], self.y[idx]\n",
    "    \n",
    "    \n",
    "train_ds = HousingDataset(\"train\")\n",
    "valid_ds = HousingDataset(\"valid\")\n",
    "test_ds = HousingDataset(\"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([-0.2981,  0.3523, -0.1092, -0.2506, -0.0341, -0.0060,  1.0806, -1.0611]),\n",
       " tensor([1.5140]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "batch_size = 16\n",
    "train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(valid_ds, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_ds, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自定义 Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomizedLinear(nn.Module):\n",
    "    def __init__(self, in_features, out_features):\n",
    "        super().__init__()\n",
    "        # 感兴趣的可以在这里实现其他的初始化方法\n",
    "        self.weights = nn.Parameter(torch.zeros(in_features, out_features))\n",
    "        self.weights = nn.init.xavier_normal(self.weights)\n",
    "        self.bias = nn.Parameter(torch.zeros(out_features))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return torch.mm(x, self.weights) + self.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#回归模型我们只需要1个数\n",
    "\n",
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self, input_dim=8):\n",
    "        super().__init__()\n",
    "        self.linear_relu_stack = nn.Sequential(\n",
    "            CustomizedLinear(input_dim, 30),\n",
    "            nn.ReLU(),\n",
    "            CustomizedLinear(30, 1)\n",
    "            )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # x.shape [batch size, 8]\n",
    "        logits = self.linear_relu_stack(x)\n",
    "        # logits.shape [batch size, 1]\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            patience (int, optional): Number of epochs with no improvement after which training will be stopped.. Defaults to 5.\n",
    "            min_delta (float, optional): Minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute \n",
    "                change of less than min_delta, will count as no improvement. Defaults to 0.01.\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.min_delta = min_delta\n",
    "        self.best_metric = -1\n",
    "        self.counter = 0\n",
    "        \n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:\n",
    "            # update best metric\n",
    "            self.best_metric = metric\n",
    "            # reset counter \n",
    "            self.counter = 0\n",
    "        else: \n",
    "            self.counter += 1\n",
    "            \n",
    "    @property\n",
    "    def early_stop(self):\n",
    "        return self.counter >= self.patience\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "@torch.no_grad()\n",
    "def evaluating(model, dataloader, loss_fct):\n",
    "    loss_list = []\n",
    "    for datas, labels in dataloader:\n",
    "        datas = datas.to(device)\n",
    "        labels = labels.to(device)\n",
    "        # 前向计算\n",
    "        logits = model(datas)\n",
    "        loss = loss_fct(logits, labels)         # 验证集损失\n",
    "        loss_list.append(loss.item())\n",
    "        \n",
    "    return np.mean(loss_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1027662/681255872.py:6: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_.\n",
      "  self.weights = nn.init.xavier_normal(self.weights)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "467d550b184e4ccbb3ad33243db6f760",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/72600 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 42 / global_step 30492\n"
     ]
    }
   ],
   "source": [
    "# 训练\n",
    "def training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    tensorboard_callback=None,\n",
    "    save_ckpt_callback=None,\n",
    "    early_stop_callback=None,\n",
    "    eval_step=500,\n",
    "    ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "    \n",
    "    global_step = 0\n",
    "    model.train()\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # training\n",
    "            for datas, labels in train_loader:\n",
    "                datas = datas.to(device)\n",
    "                labels = labels.to(device)\n",
    "                # 梯度清空\n",
    "                optimizer.zero_grad()\n",
    "                # 模型前向计算\n",
    "                logits = model(datas)\n",
    "                # 计算损失\n",
    "                loss = loss_fct(logits, labels)\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    " \n",
    "                loss = loss.cpu().item()\n",
    "                # record\n",
    "                \n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss, \"step\": global_step\n",
    "                })\n",
    "                \n",
    "                # evaluating\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss, \"step\": global_step\n",
    "                    })\n",
    "                    model.train()\n",
    "\n",
    "                    # 早停 Early Stop\n",
    "                    if early_stop_callback is not None:\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict\n",
    "                    \n",
    "                # udate step\n",
    "                global_step += 1\n",
    "                pbar.update(1)\n",
    "                pbar.set_postfix({\"epoch\": epoch_id})\n",
    "        \n",
    "    return record_dict\n",
    "        \n",
    "\n",
    "epoch = 100\n",
    "\n",
    "model = NeuralNetwork()\n",
    "\n",
    "# 1. 定义损失函数 采用MSE损失\n",
    "loss_fct = nn.MSELoss()\n",
    "# 2. 定义优化器 采用SGD\n",
    "# Optimizers specified in the torch.optim package\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# 3. early stop\n",
    "early_stop_callback = EarlyStopCallback(patience=10, min_delta=1e-3)\n",
    "\n",
    "model = model.to(device)\n",
    "record = training(\n",
    "    model, \n",
    "    train_loader, \n",
    "    val_loader, \n",
    "    epoch, \n",
    "    loss_fct, \n",
    "    optimizer, \n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=len(train_loader)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "#画线要注意的是损失是不一定在零到1之间的\n",
    "def plot_learning_curves(record_dict, sample_step=500):\n",
    "    # build DataFrame\n",
    "    train_df = pd.DataFrame(record_dict[\"train\"]).set_index(\"step\").iloc[::sample_step]\n",
    "    val_df = pd.DataFrame(record_dict[\"val\"]).set_index(\"step\")\n",
    "\n",
    "    # plot\n",
    "    for idx, item in enumerate(train_df.columns):\n",
    "        plt.plot(train_df.index, train_df[item], label=f\"train_{item}\")\n",
    "        plt.plot(val_df.index, val_df[item], label=f\"val_{item}\")\n",
    "        plt.grid()\n",
    "        plt.legend()\n",
    "        # plt.xticks(range(0, train_df.index[-1], 10*sample_step), range(0, train_df.index[-1], 10*sample_step))\n",
    "        plt.xlabel(\"step\")\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "plot_learning_curves(record)  #横坐标是 steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:     0.3282\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "loss = evaluating(model, val_loader, loss_fct)\n",
    "print(f\"loss:     {loss:.4f}\")"
   ]
  }
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